Table of Contents
Quick Verdict
A developer asks your code-assist chatbot to explain why a race condition is occurring in their goroutine pool. This requires more than code generation — it requires understanding concurrency concepts, analysing the specific code, and explaining the fix in clear natural language. DeepSeek Coder scores 8.6 on multi-turn conversation quality versus CodeLlama’s 7.2, reflecting substantially better ability to maintain context and explain complex programming concepts on a dedicated GPU server.
DeepSeek also generates code tokens faster (43 tok/s versus 37 tok/s). CodeLlama’s advantage is narrower: its explanations score 7.6/10 versus DeepSeek’s 7.2/10 on standalone quality, but the multi-turn coherence gap more than compensates.
Full data below. More at the GPU comparisons hub.
Specs Comparison
Nearly identical architectures make these models functionally interchangeable from a hardware perspective. The differences are entirely in training data and fine-tuning strategy.
| Specification | CodeLlama | DeepSeek Coder |
|---|---|---|
| Parameters | 34B | 33B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 16K | 16K |
| VRAM (FP16) | 68 GB | 66 GB |
| VRAM (INT4) | 20 GB | 19 GB |
| Licence | Meta Community | MIT |
Guides: CodeLlama VRAM requirements and DeepSeek Coder VRAM requirements.
Code-Assist Chatbot Benchmark
Tested on an NVIDIA RTX 3090 with vLLM, INT4 quantisation, and continuous batching. Conversations covered debugging sessions, architecture discussions, and code review with 4-10 turns per session. See our tokens-per-second benchmark.
| Model (INT4) | Code tok/s | Explanation Quality | Multi-turn Score | VRAM Used |
|---|---|---|---|---|
| CodeLlama | 37 | 7.6/10 | 7.2 | 20 GB |
| DeepSeek Coder | 43 | 7.2/10 | 8.6 | 19 GB |
DeepSeek Coder’s 1.4-point multi-turn advantage means it tracks conversation context better across extended debugging sessions — remembering earlier code snippets, building on previous explanations, and avoiding contradictions. This is exactly what a code-assist chatbot needs. See our best GPU for LLM inference guide.
See also: CodeLlama vs DeepSeek Coder for Chatbot / Conversational AI for a related comparison.
See also: Whisper vs Faster-Whisper for Document Processing / RAG for a related comparison.
Cost Analysis
Near-identical hardware requirements mean cost is not a differentiator. Choose on quality.
| Cost Factor | CodeLlama | DeepSeek Coder |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 20 GB | 19 GB |
| Est. Monthly Server Cost | £99 | £144 |
| Throughput Advantage | 3% faster | 10% cheaper/tok |
See our cost-per-million-tokens calculator.
Recommendation
Choose DeepSeek Coder for code-assist chatbots. Its 1.4-point multi-turn advantage translates directly into better debugging conversations, more coherent code reviews, and more useful architectural discussions. Its MIT licence also simplifies commercial deployment.
Choose CodeLlama if standalone explanation quality (7.6 versus 7.2) matters more than multi-turn coherence for your use case — for example, a one-shot code explanation endpoint rather than an interactive chat interface.
Deploy on dedicated GPU servers for a private, secure code assistant.
Deploy the Winner
Run CodeLlama or DeepSeek Coder on bare-metal GPU servers with full root access, no shared resources, and no token limits.
Browse GPU Servers
